class_docstr: A Linear Gaussian Bayesian Network is a Bayesian Network, all
  of whose variables are continuous, and where all of the CPDs
  are linear Gaussians.
  
  An important result is that the linear Gaussian Bayesian Networks
  are an alternative representation for the class of multivariate
  Gaussian distributions.
class_docstr_tok: ["A","Linear","Gaussian","Bayesian","Network","is","a","Bayesian","Network",",","all","of","whose","variables","are","continuous",",","and","where","all","of","the","CPDs","are","linear","Gaussians",".","An","important","result","is","that","the","linear","Gaussian","Bayesian","Networks","are","an","alternative","representation","for","the","class","of","multivariate","Gaussian","distributions","."]
docstr: Checks the model for various errors. This method checks for the following
  error -
  
  * Checks if the CPDs associated with nodes are consistent with their parents.
  
  Returns
  -------
  check: boolean
      True if all the checks pass.
docstr_tok: ["Checks","the","model","for","various","errors",".","This","method","checks","for","the","following","error","-","*","Checks","if","the","CPDs","associated","with","nodes","are","consistent","with","their","parents",".","Returns","--","--","--","-","check",":","boolean","True","if","all","the","checks","pass","."]
code: def check_model(self):
      """
      Checks the model for various errors. This method checks for the following
      error -
  
      * Checks if the CPDs associated with nodes are consistent with their parents.
  
      Returns
      -------
      check: boolean
          True if all the checks pass.
  
      """
      for node in self.nodes():
          cpd = self.get_cpds(node=node)
  
          if isinstance(cpd, LinearGaussianCPD):
              if set(cpd.evidence) != set(self.get_parents(node)):
                  raise ValueError(
                      "CPD associated with %s doesn't have "
                      "proper parents associated with it." % node
                  )
      return True
  
file_path: pgmpy/pgmpy/models/LinearGaussianBayesianNetwork.py
imports: import networkx
  import numpy
  from pgmpy.factors.continuous import LinearGaussianCPD
  from pgmpy.factors.distributions import GaussianDistribution
  from pgmpy.global_vars import logger
  from pgmpy.models import BayesianNetwork
label_desc: Use image node_id 5 for calling the LinearGaussianBayesianNetwork obj's underlying member method code with example usage: obj.check_model() and returns: True